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In many applications the numerical inaccuracies that can occur when using floats don t matter, and in any case are far outweighed by the speed of calculation that floats offer But in some cases we prefer the opposite trade-off, and want complete accuracy, even at the cost of speed The decimal module provides immutable Decimal numbers that are as accurate as we specify Calculations involving Decimals are slower than those involving floats, but whether this is noticeable will depend on the application To create a Decimal we must import the decimal module For example:

>>> import decimal >>> a = decimalDecimal(9876)

In some cases, each task corresponds to a distinct call to a function Defining a task for each function call leads to what is sometimes called a functional decomposition Another place to find tasks is in distinct iterations of the loops within an algorithm If the iterations are independen t and there are enough of them, then it might work well to base a task decomposition on mapping each iteration onto a task This style of task based decomposition leads to what are sometimes called loop splitting algorithms Tasks also play a key role in data driven decompositions In this case, a large data structure is decomposed and multiple units of execution concurrently update different chunks of the data structure In this case, the tasks are those updates on individual chunks

>>> b = decimalDecimal("54321012345678987654321") >>> a + b Decimal('64197012345678987654321')

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each table that holds the primary key This is not necessarily a bad thing, but it means that you must look up the data type and column name whenever you want to add another column with a foreign key or you need to write a piece of code to join tables Wouldn t it be nice if all your tables had their PKs in columns having the same name For example, every table in your database could be given a column named objectid and that column could simply have an arbitrary unique integer In this case, you can use an identity column in SQL Server to manage your integer PK value An identity column is one that automatically increments a number with each insert into the table When you make a column an identity, you specify a seed, or starting value, and an increment, which is the number to add each time a new record is added Most commonly, the seed and increment are both set to 1, meaning that each new row will be given an identity value that is 1 higher than the preceding row Another option for an arbitrary PK is a GUID GUIDs are most often used as PKs when you need to copy data between databases and you need to be sure that data copied from another database does not conflict with existing data If you were instead to use identities, you would have to play with the seed values to avoid conflicts; for example, the number 1,000,454 could easily have been used in two databases, creating a conflict when the data is copied The disadvantages of GUIDs are that they are larger than integers and they are not easily readable for humans Also, PKs are often clustered, meaning that they are stored in order Because GUIDs are random, each time you add data it ends up getting inserted into the middle of the PK, and this adds overhead to the operation In 10 we talk more about clustered versus nonclustered PKs Of all the PK options we have discussed, we most often use identity columns They are easy to set up and they provide consistency across tables No matter what method you use, carefully consider the pros and cons Implementing a PK in the wrong way not only will make it difficult to write code against your database but also could lead to degraded performance.

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Also keep in mind the forces given in the Forces section:

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